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rn AI into measurable impact. Hear bold keynotes, join hands-on sessions, and leave with strategies you can use immediately. Register for Glean:GO to: Hear from leaders at OpenAI, NVIDIA, Snowflake, and more Attend personalized hands-on trainings and breakouts with experts Learn best practices for driving AI adoption and ROI Image source: OpenAI The Rundown: OpenAI rolled out ChatGPT for Teens, a new mode that pushes users flagged as underage by the company’s systems (or who admit to being 13-17

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COMPUTE Everybody has been expecting for Cerebras Systems, one of the second-generation of AI hardware startups that has been trying very hard to compete with Nvidia on AI compute for years, to launch its latest systems, dubbed the CS-4, sometime this summer. I pegged the CS-4 announcement at August 2026 when I was contemplating what Cerebras might do with its $1.1 billion funding round back in October 2025, which was before the company went public and when it was not even clear that Cerebras wo

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DeepSeek-V4-Pro is a 1.6-trillion-parameter Mixture-of-Experts (MoE) model released with both FP8 and FP4 weights. Models at this scale naturally benefit from accelerators such as NVIDIA Blackwell GPUs, which offer more HBM, higher compute throughput, and native FP4 Tensor Cores. Yet H20 GPUs remain widely deployed, despite lacking those advantages. Hardware constraints do not relax serving requirements. Long-context prefill must still control time to first token (TTFT). Interactive decode must

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, and my guide to how OpenAI Kills Oracle pairs nicely with my Hater's Guide To Oracle, as well as the Hater’s Guide To Oracle (Part 2). I've even done a two part Hater's Guide to NVIDIA. If you want to get in touch — and especially if you have any juicy information about Anthropic, OpenAI, or any other companies in the AI bubble — hit me up on Signal at ezitron.76. I’m also on IB on The Terminal. I’m not trying to be a buzzkill here, but I have meaningful concerns about OpenAI’s ability to surv

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enue was $4 billion of that, up by a factor of 4X compared to fiscal 2025. It is hard to say how accretive this AI business is to earnings. Cisco cannot make much margin reselling Nvidia components and designs, but it can do fine selling its own chippery, systems, and software. Looking ahead to fiscal 2027, Cisco expects for sales to be between $72.2 billion and $73.4 billion, and that is up 15 percent at the midpoint of $72.8 billion. It is hard to say how much profits Cisco can extract from th

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ks It won't kill you, or humanity, to get off the AI upgrade treadmill New models arrive so fast, we already have more new tech than it's possible to understand in a career Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play in

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ks It won't kill you, or humanity, to get off the AI upgrade treadmill New models arrive so fast, we already have more new tech than it's possible to understand in a career Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play in

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ks It won't kill you, or humanity, to get off the AI upgrade treadmill New models arrive so fast, we already have more new tech than it's possible to understand in a career Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play in

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type — you can use the library directly for academic or industry work without adopting SGLang's runner stack. SGLang will continue to develop the mechanism, and we expect the standalone library and the in-tree implementation to converge over time. Acknowledgments This work was a collaboration between the SGLang team and the Meta team. Meta: Shiyang Chen , Lianmin Zheng We also thank the NVIDIA, AMD, Thinking Machines Lab, and Meta PyTorch teams for their help along the way. ( Equal contribution)

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ard into permissive licensing. (SCMP); Ant’s AGI lab open-sourced Ling-3.0-tiny (7.9B total / 1.3B active parameters), designed to run agentic RL post-training locally on a single Nvidia DGX Spark rather than a cluster. (@AntLingAGI); MiniMax released MiniMax-Music3, an open-weights, production-ready music generation model. (@MiniMax AI); Enterprise AI costs hit a 2026 low, per new research, as price wars and Chinese open-source models compress what companies pay to deploy AI. (SCMP) Funding Bey

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million GPUs across its fleet. CoreWeave has 3,700 MW of contracted power to support its buildouts for the big model builders and Microsoft. Thanks to its tight relationship with Nvidia, which has been fostering the neoclouds to put competitive pressure on the big clouds who are all making their own CPUs and accelerators, CoreWeave is the first company to bring up a “Vera-Rubin” NVL72 system, which it did during Q2 2026. Nebius is a lot smaller than CoreWeave, but growing fast and building its

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structure built for them. Crusoe Managed Inference serves the open catalog end to end on the Crusoe Intelligence Foundry: GLM, Llama, DeepSeek, Qwen, Gemma, gpt-oss, Kimi, and the NVIDIA Nemotron™ 3 family, with day-zero support for new releases. Serving open weights is half of that work. The other half is meeting developers in the open source tools they already use. Crusoe is an upstream provider in LiteLLM and now a native Pydantic AI provider. Each integration follows the same principle: cont

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s, 55% of DeepSeek's and 39% of Z.ai's. No large American account looks like this: Google, Microsoft and IBM Granite record essentially none of their 2026 downloads above 70B, and NVIDIA and Meta only 14% and 9%. The difference becomes clearer in total downloads. Moonshot’s frontier-only portfolio recorded 37M downloads over the year, while Qwen’s broader release strategy across model sizes reached 2,045M (across repositories with declared parameter counts, 2,061M including all repositories) , a

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rainer("lerobot local") returns a Trainer that works like create policy() , and a TrainSpec describes the run; the record-train-deploy loop then closes in a few lines: On a single NVIDIA L4 ( g6.4xlarge ), 500 optimizer steps of ACT (51.6M parameters, effective batch size 8) over a 120-frame episode completed in 133 seconds and wrote a checkpoint that create policy() loads back through the same entry point used to run any other policy. Training time scales with dataset size, batch size, and step

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$10.84 billion then, by the math, which is a 92.2 percent increase year on year. But, more than anything else, Supermicro has become a go-to maker of accelerated systems based on Nvidia CPUs, GPUs, and scale up and scale out switching, and that is a very large portion of that overall system sales at this point. But, in the fourth quarter, as Supermicro’s sales of AI systems, as it calls machines based on GPU (and maybe someday XPU) accelerators as well as CPU-only systems that are running agent

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d as one of the community’s best.\n\n“A Coin Flip for Safety” was reproduced using AlphaXiv’s OpenResearch with OpenCode’s free BigPickle model, with experiments run locally on 2× NVIDIA RTX 3090s.\n\nIt was a great demonstration of how agent-assisted research can make independent reproduction and auditing accessible with relatively modest compute.\n\nThanks to the Hugging Face and AlphaXiv teams for organizing the challenge and making the whole process open and reproducible!\n","html":" Really

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oken count: --no-split is the single fused kernel, --split is the split pipeline and is the default because it holds up across the whole sweep above. Benchmark Environment GPU: 8× NVIDIA B200 (P0, 1965 MHz SM, 3996 MHz HBM); Driver: 580.159.03; PyTorch: 2.13.0+cu130; CUDA (PyTorch build): 13.0; cuDNN: 9.19.0; Python: 3.14.6; OS: Ubuntu 22.04.5 LTS; CPU: Intel Xeon 6760P 9. Summary The fusion rests on two tricks. Folding gate and up into the tensor map places every SwiGLU pair in the same accumul

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ks It won't kill you, or humanity, to get off the AI upgrade treadmill New models arrive so fast, we already have more new tech than it's possible to understand in a career Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play in

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sive inference on GPU. Because it answers directly instead of reasoning, it returns the first token quickly, even on image-heavy inputs. We measure time to first token on a single NVIDIA H100 SXM5 GPU, one request at a time, across three input types: a single 512x512 image, a 512x512 image with a 1,024-token input text, and 5 256x256 5-frame video clip. LFM2.5-VL-3B keeps latency consistently low and pulls far ahead on multi-frame inputs, returning the first token in about 34 ms on a 5-frame vid

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mands: the ability to see inside a model, adapt it, and retain the intelligence they build with it. This is why we are enthusiastic contributors to the Open Secure AI Alliance and NVIDIA Nemotron Coalition. We are now extending that openness beyond our own models. Mistral’s platform will support third-party open models, starting with Z.ai’s GLM-5.2. This and future open models will run on the same infrastructure, regional controls, and service commitments as Mistral models, so customers can broa

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